Coding/Learn/sql/sql select distinct
SQL & Relational Database Engineering Complete Course
01.Introduction & Relational Database Architecture
02.Syntax, Clauses & Execution Order
03.SELECT: Column Projection & Expressions
04.SELECT DISTINCT & Row Deduplication
05.WHERE Clause: Filtering Predicates & Comparison
06.ORDER BY: Sorting, Multi-Column & NULLS Ordering
07.Logical Operators: AND, OR, NOT & Operator Precedence
08.Comments: Single-Line (--), Multi-Line (/* */) & Query Hints
09.Operators: Arithmetic, Bitwise & String Concatenation
10.LIKE Operator & Wildcard Characters (%, _)
11.IN & NOT IN: Membership Operators & Subqueries
12.BETWEEN: Range Filtering for Numbers, Dates & Text
13.NULL Values: Three-Valued Logic, IS NULL & IS NOT NULL
14.Aliases: AS Keyword for Columns & Tables
15.SELECT TOP, LIMIT & OFFSET Pagination
16.INSERT INTO: Single & Multi-Row Insertions
17.UPDATE: Modifying Existing Records & Safe WHERE Rules
18.DELETE: Removing Rows vs TRUNCATE TABLE
19.SELECT INTO & Table Backups (CREATE TABLE AS SELECT)
20.INSERT INTO SELECT: Bulk Data Migration
21.Aggregate Functions: Statistical Summaries
22.MIN() & MAX(): Finding Extremes Across Types
23.COUNT: COUNT(*), COUNT(column) & COUNT(DISTINCT)
24.SUM() & AVG(): Numerical Aggregation & Averages
25.GROUP BY: Aggregation Buckets & Categorical Metrics
26.HAVING: Filtering Aggregated Groups vs WHERE
27.Joins Architecture: Relational Modeling & Venn Diagrams
28.INNER JOIN: Matching Intersections Across Tables
29.LEFT JOIN: Preserving Unmatched Left Rows & Finding Orphans
30.RIGHT JOIN: Preserving Unmatched Right Rows
31.FULL JOIN: Full Outer Join & Complete Reconciliation
32.SELF JOIN: Hierarchies, Trees & Parent-Child Relationships
33.UNION: Combining Result Sets with Automatic Deduplication
34.UNION ALL vs UNION: Performance & Duplicates
35.EXISTS & NOT EXISTS: Correlated Subqueries
36.ANY & ALL: Quantified Comparison Subqueries
37.CASE: Conditional Expressions & Data Transposition
38.Null Functions: COALESCE, IFNULL, ISNULL & NULLIF
39.Stored Procedures, Functions & Query Parameters
40.CREATE & DROP DATABASE: Environment Lifecycle
41.CREATE TABLE: Defining Schemas & Types
42.DROP & TRUNCATE TABLE: Structure vs Data Purging
43.ALTER TABLE: Modifying Columns & Schema Evolution
44.AUTO INCREMENT & Sequences: Identity Generation
45.Date & Time Formats: Timestamp Manipulation
46.Views: Virtual Tables & Security Layers
47.Constraints Overview: Relational Integrity
48.NOT NULL & UNIQUE: Data Invariants
49.PRIMARY KEY: Entity Uniqueness & Clustering
50.FOREIGN KEY: Referential Integrity & Cascades
51.CHECK & DEFAULT: Business Rules at DB Layer
52.CREATE INDEX: B-Trees & Query Optimization
53.Injection Defense: Threat Models & Prevention
54.Parameters & Prepared Statements: Safe Execution
55.Database Engines & Hosting Architecture
56.Dialect References & Data Type Standards
57.Production Best Practices & Query Tuning Checklist
58.Interactive Exercises: Multi-Table Analytics Challenges
59.Comprehensive Assessment & Knowledge Check
60.Capstone Project: Enterprise Analytics Engine
61.Mastery Certification & Graduate Verification
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LearnSQL SELECT DISTINCT & Row Deduplication
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Lesson 04 of 61
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SQL SELECT DISTINCT & Row Deduplication

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In Plain English

The `SELECT DISTINCT` clause eliminates duplicate rows from the query output, returning only unique values or combinations. `DISTINCT` operates across all projected columns simultaneously.

The Mental Model

"If a guest list has someone's name written five times because they RSVP'd repeatedly, `SELECT DISTINCT` hands you a deduplicated list with their name appearing exactly once."

Deep Dive: How It Works

Multi-Column DISTINCT: `SELECT DISTINCT country, city FROM customers` evaluates uniqueness on the COMBINATION of country and city together.

Performance Impact: Deduplication requires sorting or building a hash set of results, adding memory and CPU overhead to large queries.

COUNT(DISTINCT col): Calculates the cardinality (number of unique non-null values) in a dataset.

Syntax Blueprint

SELECT DISTINCT column1, column2
FROM table_name;

DISTINCT evaluates across all columns listed in the SELECT clause.

PointCore Rules to Remember

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Evaluates All Columns: `SELECT DISTINCT a, b` returns unique (a, b) tuples, not just distinct a values.
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NULL Handling: Multiple NULLs are treated as duplicates of each other; DISTINCT returns a single NULL row.
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Cardinality Inspection: Use `COUNT(DISTINCT category)` to find how many unique categories exist in a table.

Common Beginner Traps & How to Fix Them

Mistake:Writing `SELECT DISTINCT(col1), col2` thinking DISTINCT applies only to col1.

Why it happens: DISTINCT is a clause keyword modifying the entire row projection, not a function.

How to fix: To group by one column and pick one arbitrary value from another, use `GROUP BY`.

Pro Tip: Always verify if an existing index covers the distinct columns; indexed B-Trees can satisfy DISTINCT without sorting.

Live Interactive Example

Hit Run Code to see it live
Querying Unique Categories and Multi-Column Pairs
Python 3.12
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Output Console
Click "Run Code" to view the rendered output.
How it works: DISTINCT reduced the 5 orders down to 2 distinct countries and 3 unique country/city combinations.

Your Turn: Micro Challenge

No pressure! Edit the starter code below and test your solution with instant feedback.

Micro Exercise

Select Unique Departments

  • PointQuery distinct department names from the `staff` table.
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Sandbox Output
Click "Run & Check" to test your solution.

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